arXiv AI

What General Intelligence Requires: Non-Reducible Constraints Across Levels of Description

arXiv:2607. 18943v1 Announce Type: new Abstract: General intelligence, of the kind that underwrites the full range of human cognitive achievement, is not a property of computational architecture alone.

arXiv AI
Jun 12

From AGI to ASI

arXiv:2606. 12683v1 Announce Type: new Abstract: Over the last decade, building human-level artificial general intelligence has moved from far-fetched speculation to being a concrete next-decade target for many of the largest AI organisations.

By Tim Genewein, Matija Franklin, Alexander Lerchner, Laurent Orseau, Samuel Albanie, Adam Bales, Cole Wyeth, Stephanie Chan, Iason Gabriel, Joel Z. Leibo, Allan Dafoe, Marcus Hutter, Thore Graepel, Shane Legg
arXiv AI
Jul 1

A Technical Typology of AI Systems in Public Administration

arXiv:2606. 31755v1 Announce Type: cross Abstract: Research on artificial intelligence (AI) in the public sector often treats "AI" as a single category, neglecting technical distinctions between different AI systems.

By Jonathan Rystr{\o}m, Chris Schmitz, Nathan Davies, Gerhard Hammerschmid, Albert Meijer, Chris Russell
arXiv AI
22h ago

ASI-Bench: At the Dawn of Artificial Superintelligence

arXiv:2608. 17271v1 Announce Type: new Abstract: Artificial superintelligence (ASI) requires AI to move beyond mastering existing knowledge toward exploring the unknown, creating new knowledge, and turning new ideas into verifiable results.

By Junwei Zhou, Zhen Sun, Binyu Li, Jiangyu Zhou, Yuexi Pan, Hengyu Wang, Honghe Ren, Xiaohan Jia, Xueyang Zhou, Xiaoyu Cao, Yongchao Chen, Yuanning Feng, Junhao Wu, Cheng Zhang, Sijia Chen, Haoyu Xue, Chengsong You, Huan Wang, Koutian Wu, Peigan Gao, Jiakun Wu, Wenzhe Li, Ergan Shang, Qingyuan Zheng, Jingjing Zhou, Ruixuan Jia, Yan Xu, Hongrui Zhang, Xiao-Han Ma, Zhengxiang Cheng, Yuexing Hao, Liting Mai, Xianglin Ji, Wenjun Zhang, Zhuofan Chen, Yixiao Huang, Chi Wang, Wenyue Hua, Yilun Hao, Yuantao Zhai, Ziyan Zhao, Jingyan Xie
arXiv AI
Jun 26

Radical AI Interpretability

arXiv:2606. 26523v1 Announce Type: new Abstract: We develop a framework for interpreting AI systems as agents, drawing on the philosophical tradition of radical interpretation and the tools of mechanistic interpretability.

By Daniel A. Herrmann, Benjamin A. Levinstein